Professor Nikola Biller-Andorno is a leading academic in biomedical ethics and digital health innovation at the University of Zurich , where she directs the Institute of Biomedical Ethics and History of Medicine and serves as Vice Dean for Innovation and Digitalization at the Faculty of Medicine . Her work bridges cutting-edge technology with ethical frameworks for patient-centered healthcare. Director, Institute of Biomedical Ethics and History of Medicine (WHO Collaborating Centre) Vice Dean for Innovation and Digitalization, Medical Faculty Founding Director, UZH Center for Medical Humanities Chair, PhD program 'Biomedical Ethics and Law' Research Interests focus on digital transformation in healthcare, including ethical implications of AI, patient experience integration via big data, and crisis management frameworks during pandemics. Her scholarship addresses: Digitalization of health systems Algorithmic decision-making ethics Equity in digital health access Scientific Awards include: Commonwealth Fund Harkness Fellowship (2012-2014) Fellowship at Collegium Helveticum (2016-2020) Master of Health Business Administration (2018) Leadership roles in International Association of Bioethics and European Association of Centres of Medical Ethics
Professor Manolis Gavaises is a leading academic in the field of mechanical engineering and computational fluid dynamics at City St George's, University of London, where he holds the position of Professor in the School of Engineering and Mathematical Sciences. He earned his PhD from Imperial College London and has been a faculty member since 2001, progressing to full Professor in 2009. His research is centered on advanced modeling of multi-phase flows, cavitation, and fuel injection systems, with extensive collaborations across Europe and industry partners such as Delphi, Caterpillar, and BP. Education: DIC, Mechanical Engineering, Computational Fluid Dynamics, Imperial College London, 1997 PhD, Mechanical Engineering, Computational Fluid Dynamics, Imperial College London, 1997 Diploma (5 years), Mechanical Engineering, National Technical University of Athens, 1992 His research interests span computational fluid dynamics, cavitation, fuel injection, atomization, high-pressure and supercritical flows, and alternative fuels . He has developed advanced numerical models and experimental techniques, including X-ray phase contrast imaging and high-pressure test rigs. His work integrates fundamental DNS and LES simulations with industrial applications in automotive, marine, aerospace, and medical devices such as heart valves. The recent publications reflect a strong trend toward real-fluid thermodynamic modeling (e.g., PC-SAFT), multi-component fuel behavior, cavitation erosion, and advanced diagnostics . His research increasingly incorporates machine learning and high-fidelity imaging to understand complex flow phenomena across energy, transportation, and biomedical domains. Scientific Awards and Recognitions: Richard Way Prize (1998) Arch T. Collwell Merit Award (1998) Best Oral Paper, SAE World Congress (2006) PE Publication Award, IMechE (2007) Best Presentation Award, Engine Combustion Processes (2009) Fellow, IMechE (2013) Fellow, IMA (2015) As a dedicated mentor, Professor Gavaises has supervised 13 PhDs to completion and currently guides 23 doctoral students. He has secured over €16 million in EU and UK funding, including multiple Horizon 2020 Marie Skłodowska-Curie ITN projects (CAFÉ, HAOS, IPPAD), which support 46 early-career researchers globally. He has created academic opportunities for post-docs and junior faculty, significantly advancing the research profile of his institution. He leads the International Institute of Cavitation Research (IICR), co-founded in 2011 with partners from Loughborough University, TU Delft, and Imperial College, supported by The Lloyd’s Register Foundation. His lab maintains strong experimental capabilities, including a 2000bar pressure flow rig with micro-transparent nozzles and collaborations with Argonne National Laboratory for X-ray imaging.
Dr. Ryan B Graham is an Associate Professor in the School of Human Kinetics at the University of Ottawa , with cross-appointments to the Ottawa-Carleton Institute for Biomedical Engineering . He holds Adjunct Assistant Professor positions at Queen's University and University of Waterloo , reflecting his interdisciplinary collaborations in biomechanics and biomedical engineering. His research focuses on spine movement analysis , motion capture validation , and sensor technology applications in clinical and military contexts. Recent work includes developing markerless motion capture systems, assessing movement reliability, and modeling spine dynamics for injury prevention. Editorial Contributions: Associate Editor for Biomechanics and Control of Human Movement at Frontiers in Sports and Active Living Dr. Graham's academic network includes collaborations with institutions in Canada, the UK, and the Netherlands, with a focus on advancing biomechanical methodologies and their clinical translation.
Jonathan Fan is an Associate Professor at Stanford University in the Department of Electrical Engineering. His teaching portfolio includes graduate and undergraduate courses in electromagnetics, integrated circuit fabrication, and specialized studies across all quarters. EE 242: Electromagnetic Waves (Autumn) EE 312: Integrated Circuit Fabrication Laboratory (Winter) ENGR 42/EE 42: Electromagnetics and Applications (Spring) 11 independent studies and thesis courses (EE 190, EE 191, EE 300, etc.) His research focuses on nanophotonics and metasurface engineering , with particular emphasis on inverse design methodologies, machine learning -driven photonic optimization, and machine learning in electromagnetic simulation. His recent publications demonstrate a strong trend toward deep learning-enabled photonic design and high-speed optimization of complex optical systems. His work spans metamaterial fabrication , nonlocal effects in metasurfaces, and multi-functional optical devices such as spaceplates for aberration correction. Key technical contributions include physics-augmented neural networks , reparameterization techniques for design constraints, and topology-optimized metasurfaces .
Crystal Noel is an Assistant Professor at Duke University in the Pratt School of Engineering and Trinity College of Arts & Sciences , with appointments in both the Department of Electrical and Computer Engineering and Physics since 2022. She is also a Member of the Duke Quantum Center since 2024. Ph.D. in Electrical and Computer Engineering from University of California, Berkeley (2019) B.S. in Massachusetts Institute of Technology (2013) Her research focuses on quantum computing and simulation with trapped ions , integrated photonics for scalable trapped ion systems , and electric-field noise from surfaces . Recent work includes developing non-invasive mid-circuit measurement techniques, sympathetic cooling for ion chains, and cross-platform quantum state comparison. She has secured significant grants from National Science Foundation , Rochester Institute of Technology , and Defense Advanced Research Projects Agency for quantum co-design and networking projects. Her lab ( Noel Lab ) explores scalable quantum computing architectures and surface noise mitigation. She teaches courses ranging from foundational Fields and Waves: Fundamentals of Information Propagation to advanced topics in Quantum Engineering with Atoms and Advanced Topics in Electrical and Computer Engineering .
Kayvon Fatahalian is an Associate Professor in the Department of Computer Science at Stanford University. His research focuses on real-time graphics, high-efficiency simulation engines for entertainment and AI, and large-scale image/video analysis platforms. He explores intersections of computer graphics, machine learning, and high-performance computing to advance systems for interactive applications and AI-driven tasks. His work includes innovations in rendering pipelines, embodied AI simulations, and generative models for 3D content creation. Recent projects address challenges in multi-agent systems, motion synthesis, and scalable rendering architectures. Fatahalian’s contributions span technical systems, algorithmic frameworks, and foundational research in graphics and AI. Notable areas of exploration include: Real-time rendering optimizations for complex scenes AI-driven motion and style generation from sparse inputs Efficient simulation frameworks for deep reinforcement learning Weak supervision techniques for rare category detection His publications emphasize practical systems with theoretical grounding, often bridging hardware/software co-design principles with modern AI methodologies. Current work includes developing agile hardware accelerators and scalable architectures for next-generation interactive systems.
Ji-Quan Shi is a Research Fellow in the Department of Earth Science & Engineering at Imperial College London's Faculty of Engineering. His affiliations include the Energy Futures Lab, Minerals, Energy and Environmental Engineering, and Petroleum Geoscience and Engineering. His research focuses on geomechanical and coupled THM (thermo-hydro-mechanical) modeling for CO2 storage, geothermal energy systems, and mining-induced seismicity. Key interests include induced seismicity risk assessment, reservoir simulation, and fracture mechanics in subsurface energy systems. Education background not explicitly stated in text, but his expertise spans geoscience, civil engineering, and environmental systems. Research areas emphasize interdisciplinary approaches to subsurface energy challenges, including carbon capture and storage (CCS), geothermal reservoir management, and coal mining hazards. His work combines field observations, numerical modeling, and laboratory experiments to address challenges like CO2 plume tracking, fault activation mechanisms, and microseismic event forecasting. Recent studies focus on Iceland's geothermal fields (Hellisheiði) and North African CO2 storage sites (In Salah). He has pioneered methods for integrating microseismic data with reservoir models to improve safety and efficiency in subsurface operations. Notable contributions include probabilistic frameworks for hazardous microseismicity prediction in coal mines and coupled modeling of thermal effects on induced seismicity. His research also explores innovative monitoring technologies like distributed fiber optic sensing for CO2 plume tracking.
Maria T. Schultheis is a Professor in Drexel University's Department of Psychological and Brain Sciences and holds a joint appointment in the School of Biomedical Engineering, Science and Health Systems. As Interim Director of Clinical Training, she oversees clinical psychology education. She earned her PhD in Clinical Psychology from Drexel University in 1998. Her research focuses on neurorehabilitation, particularly applying virtual reality (VR) technology to assess and improve driving capacity and everyday functioning in individuals with neurological disorders like traumatic brain injury, stroke, and multiple sclerosis. She has pioneered VR-based driving simulators for clinical evaluation and rehabilitation. Her work integrates clinical psychology, engineering, and transportation science, addressing cognitive, physical, and behavioral demands of driving post-neurological injury. Key projects include developing driving assessment protocols for disabled populations and investigating fatigue management in MS patients. Her interdisciplinary approach has been funded by NIH, NIDRR, and the NMSS. Notable awards include the 2007 APA Early Career Award (Division 40) and recognition from the National Academy of Neuropsychology. Schultheis leads the Applied Neuro-Technologies Lab, emphasizing ecologically valid methodologies. Over 35 peer-reviewed publications and presentations at international forums highlight her contributions. She serves on the National Research Council’s Transportation Research Board, influencing policy on neurological disorders and mobility. Grants have supported projects like VR-based financial competency assessments and understanding dual-task demands in post-concussion driving. Her research also explores decision-making competency in young adults and the neurocognitive correlates of risky driving behaviors. Collaborations with biomedical engineers and transportation specialists reflect her commitment to bridging clinical practice and technological innovation for functional recovery.
Dr. Dennis Buckmaster is a Professor in Agricultural & Biological Engineering at Purdue University, serving as Dean's Fellow for Digital Agriculture. He holds a B.S. from Purdue University and M.S./Ph.D. from Michigan State University. His research focuses on digital agriculture, machine systems engineering, and data science applications in farming. He co-coordinates the Agricultural Systems Management program and teaches courses like Computing Technology with Applications and Ag Tech and Innovation. He leads the Open Ag Technology and Systems Center (OATS Center), advancing open-source solutions for agriculture through platforms like ISOBlue and OADA. His work integrates IoT, robotics, and machine learning to optimize crop production, livestock management, and farm decision-making. He has authored over 150 publications on precision agriculture technologies. Professional memberships include American Society of Agricultural and Biological Engineers and Fluid Power Society. He emphasizes data interoperability, edge computing, and bridging engineering with agricultural practices through collaborative frameworks like LATTICE and Meta Ag.
Simon Mak is an Assistant Professor of Statistical Science at Duke University and a Faculty Network Member of the Duke Institute for Brain Sciences. His educational background includes: Ph.D. in Statistics, Georgia Institute of Technology (2018) M.S. in Statistics, Georgia Institute of Technology (2018) B.S. in Statistics, Simon Fraser University (2013) Dr. Mak's research focuses on advanced statistical methodologies for complex scientific problems. His expertise spans statistical modeling , Bayesian inference , Gaussian process emulation , and uncertainty quantification . He applies these methods to nuclear physics (heavy-ion collisions), engineering (engine control systems), and music information retrieval, emphasizing scalability and interpretability in scientific computing. Analysis of his 2023-2025 publications reveals dominant trends in scalable Gaussian process methods for massive datasets and multi-fidelity simulations, particularly applied to high-energy physics and engineering systems. He has pioneered innovations in Bayesian optimization for expensive simulators and developed novel frameworks for online change-point detection in streaming data, demonstrating exceptional cross-disciplinary impact. Dr. Mak leads multiple significant research initiatives: Collaborative Research: Cost-Efficient and Confident Sampling for Modern Scientific Discovery (2023-2026) Science-Integrated Predictive modeLing (SCINPL) for scalable scientific computing (2022-2025) The X-SCAPE collaboration for statistically advanced nuclear collision modeling (2020-2025) These projects fund his development of statistical frameworks for scientific discovery in complex systems. He actively contributes to the JETSCAPE collaboration, developing multi-stage frameworks for studying jet quenching in heavy-ion collisions, and applies statistical methods through the Duke Institute for Brain Sciences to advance neuroscience research.
Mo Jiang is a Researcher in the Department of Chemical & Life Science Engineering at Virginia Commonwealth University's College of Engineering. His research focuses on advanced crystallization processes for energy storage materials and pharmaceutical manufacturing. He specializes in continuous manufacturing techniques such as slug-flow reactors, aiming to improve material uniformity, scalability, and process efficiency. His work bridges chemical engineering principles with practical applications in battery technology and drug substance development. Research Interests: Continuous crystallization and manufacturing systems Slug-flow synthesis of battery cathode materials Process optimization for pharmaceuticals and energy storage Scalable synthesis of uniform microcrystals His recent articles highlight advancements in low-cobalt/cobalt-free lithium-ion battery cathodes, pharmaceutical crystallization methods, and the application of computational fluid dynamics to enhance manufacturing processes. These studies emphasize improving material performance, reducing costs, and achieving sustainable production methods. While no formal academic awards are listed, his prolific publication record demonstrates expertise in interdisciplinary engineering solutions. He collaborates on projects involving process design, real-time monitoring, and the integration of advanced manufacturing technologies.
Pascale Biron is a Professor in the Department of Geography, Urban Planning and Environment at Concordia University, Montreal. She holds a Ph.D. in Geography from Université de Montréal (1995) and has been with Concordia since 1998. Her research focuses on river dynamics, stream restoration for fish habitat, flood modeling, and climate change impacts. She specializes in hydrogeomorphology, river management in agricultural watersheds, and numerical modeling of fluvial processes. Research Interests: Her work includes river restoration strategies, flood risk assessment using LiDAR technology, and the 'river freedom' concept promoting ecosystem resilience. She collaborates closely with government agencies to translate research into practical river management policies. Professional Affiliations: Canadian Geomorphology Research Group, Canadian Association of Geographers, American Geophysical Union, GRIL (Limnology Research Group), and RIISQ (Quebec Flood Risk Network). Publications & Research: Recent studies address global salmonid biomass patterns, fluvial hazard detection via machine learning, and large-scale flood modeling. She supervises 19 graduate students in Ph.D./M.Sc. programs in Geography and Environmental Studies, focusing on topics like river confluence hydraulics and agricultural stream restoration. Grants & Funding: Active projects include river dynamics in fish habitats, flood modeling for road infrastructure vulnerability, and computational fluid dynamics simulations of river flows. She also leads research on societal dimensions of river restoration and policy frameworks for flood resilience.
Giuseppe Rizzo is a Full Professor at the Department of Maternal and Child Health and Urological Sciences, Sapienza University of Rome. His academic career focuses on Maternal Fetal Medicine, with expertise in ultrasound applications, fetal growth restriction, preeclampsia, and congenital anomalies. Research Interests : Delivery optimization, prematurity prediction, ultrasound diagnostics, and placental anomalies. Recent Publications : Over 15 high-impact studies in 2024-2025 on topics like umbilical cord abnormalities, cerebroplacental ratios, and gestational diabetes complications. Clinical Expertise : Prenatal counseling, Doppler ultrasound, and labor management protocols. Collaborations : Active in multicenter trials across Italy and Europe, with a focus on fetal neurosonology and high-fidelity obstetric simulation.
Mohammad Hamdaqa is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he leads the Laboratory of Software and Emerging Technologies. His academic journey includes a Ph.D. in Electrical and Computer Engineering from the University of Waterloo (2016), a Master's in Electrical and Computer Engineering from Concordia University, an MBA from the New York Institute of Technology, and a Bachelor's in Computer Engineering from Jordan University of Science and Technology. His research focuses on the intersection of software engineering and emerging technologies, particularly examining how software engineering approaches can be adapted for complex new platforms like cloud computing and blockchain. His work spans model-driven software engineering, cloud application architecture, smart contract development, and infrastructure as code. He investigates both how traditional software engineering practices can evolve to address the challenges of modern distributed systems and how emerging technologies can transform software development processes themselves. Analysis of his recent publications reveals a strong emphasis on blockchain technologies (particularly smart contracts), cloud-native applications, and the application of AI to software engineering tasks. His work shows a consistent thread of empirical research combined with practical tool development, with increasing focus on sustainability aspects of software systems in recent years. Much of his research bridges theoretical foundations with practical implementation concerns. Professor Hamdaqa serves as a thesis supervisor for multiple graduate students, with recent completed Master's theses focusing on smart contract auditing, prompt engineering for OCL generation, model-driven epidemiology, and security practices in infrastructure as code. He actively recruits students for research projects in his laboratory. He is a member of both the IEEE Computer Society and the Association for Computing Machinery (ACM), has served on program committees for major software engineering conferences, and is on the editorial board of Service Transaction on Internet of Thing. His laboratory, the Laboratory of Software and Emerging Technologies, serves as the hub for his research activities in blockchain, cloud computing, and model-driven engineering.
Thomas Rüde is Universitätsprofessor for Hydrogeology at RWTH Aachen University , Germany, where he leads the Hydrogeology group within the Faculty of Georesources and Materials Engineering. Holding the chair since 2005, he also serves as Managing Director of the Vereinigung Aachener Geowissenschaftler e.V. and has previously been Vice-President (2008-2014) and Executive Council member (2000-2008) of the International Mine Water Association (IMWA). Education 2004 – Privatdozent (Dr. rer. nat. habil.), University of Munich 1995 – Dr. rer. nat., University of Karlsruhe 1991 – Diplom-Geologe, University of Karlsruhe Research focus Professor Rüde’s work centres on understanding and modelling flow and reactive transport in complex aquifer systems . Key themes include: Contaminant hydrogeology – behaviour of geogenic arsenic and uranium in groundwater Groundwater protection and remediation – risk assessment and mitigation strategies Mine-water management – acid mine drainage, dewatering-well clogging, post-mining landscapes Tracer and hydraulic testing – field experiments to quantify subsurface heterogeneity Numerical modelling – high-performance simulation of multi-aquifer systems and karst His research spans Europe (Germany, Austria, Netherlands), Latin America (Mexico, Indonesia) and South-East Asia, frequently in close collaboration with local universities and industry partners. Publication trends Since 2010, Rüde has published extensively on geogenic contamination (As, U, F) in sedimentary and volcanic aquifers, mine-water impacts , and karst hydraulics . Recent work (2022-24) highlights advanced environmental tracers (gadolinium), transboundary groundwater issues, and the sustainable management of post-mining landscapes under climate change. Numerical models range from site-scale dewatering optimisation to catchment-scale coupled flow-transport simulations. Scientific awards & recognition Best Teaching Award 2010 – RWTH Aachen University Best Teaching Award 2012 – RWTH Aachen University Best Teaching Award 2014 – RWTH Aachen University Supervision & academic service Since 1998 he has taught hydrogeology through lectures, seminars, laboratory and field courses, and computer-based modelling labs. To date he has supervised: 13 PhD candidates 34 Diploma students 57 MSc students 63 BSc students He is Chairman of the Study Commission for the BSc programme in Georesources Management at RWTH Aachen, ensuring curriculum development and quality assurance. Laboratory & field infrastructure His group operates modern hydrochemical laboratories for trace-element analyses and maintains field stations for tracer experiments in Germany, Mexico and Indonesia. High-performance computing resources (in collaboration with the Jülich Supercomputing Centre) enable large-scale groundwater modelling and Monte-Carlo uncertainty assessments.
